Connectomics is a popular approach for under-standing the brain with neuroimaging data. However, a con-nectome generated from one atlas is different in size, topology, and scale compared to a connectome generated from...
详细信息
The numerous strategies for the automated morphological categorization of galaxies, which uses a variety of supervised machine learning techniques, have not been well examined or compared. As the majority of star gala...
详细信息
In today's medical image, analyses are captured very rapidly due to early detection of the brain tumour being very important. The tumour could be plainly visible in the neurological magnetic resonance imaging stud...
详细信息
Federated Learning (FL) has emerged as a promising approach for preserving data privacy in recommendation systems by training models locally. Recently, Graph Neural Networks (GNN) have gained popularity in recommendat...
详细信息
With the growing demand for wireless networks, network traffic has surged, particularly involving large-scale data transmission. This results in an increased number of network security threats and the emergence of var...
详细信息
Designing efficient algorithms to compute a Nash Equilibrium (NE) in multiplayer games is still an open challenge. In this paper, we focus on computing an NE that optimizes a given objective function. Finding an optim...
详细信息
Quantum computing is a model of computing a new approach to perform and solve computational problems and comes up with amazing advantages such as exponential speed-up of specific tasks that lead to a revolution of ind...
详细信息
—Federated learning (FL) trains a global model across a number of decentralized users, each with a local dataset. Compared to traditional centralized learning, FL does not require direct access to local datasets and ...
ISBN:
(纸本)1891562835
—Federated learning (FL) trains a global model across a number of decentralized users, each with a local dataset. Compared to traditional centralized learning, FL does not require direct access to local datasets and thus aims to mitigate data privacy concerns. However, data privacy leakage in FL still exists due to inference attacks, including membership inference, property inference, and data inversion. In this work, we propose a new type of privacy inference attack, coined Preference Profiling A ttack (PPA), t hat accurately profiles t he p rivate p references o f a l ocal u ser, e .g., m ost liked (disliked) items from the client’s online shopping and most common expressions from the user’s selfies. I n g eneral, P PA can profile t op-k (i.e., k = 1, 2, 3 a nd k = 1 i n p articular) preferences contingent on the local client (user)’s characteristics. Our key insight is that the gradient variation of a local user’s model has a distinguishable sensitivity to the sample proportion of a given class, especially the majority (minority) class. By observing a user model’s gradient sensitivity to a class, PPA can profile the sample proportion of the class in the user’s local dataset, and thus the user’s preference of the class is exposed. The inherent statistical heterogeneity of FL further facilitates PPA. We have extensively evaluated the PPA’s effectiveness using four datasets (MNIST, CIFAR10, RAF-DB and Products-10K). Our results show that PPA achieves 90% and 98% top-1 attack accuracy to the MNIST and CIFAR10, respectively. More importantly, in real-world commercial scenarios of shopping (i.e., Products-10K) and social network (i.e., RAF-DB), PPA gains a top-1 attack accuracy of 78% in the former case to infer the most ordered items (i.e., as a commercial competitor), and 88% in the latter case to infer a victim user’s most often facial expressions, e.g., disgusted. The top-3 attack accuracy and top-2 accuracy is up to 88% and 100% for the Products-10K and RAF-DB, re
We rigorously study the joint evolution of training dynamics via stochastic gradient descent (SGD) and the spectra of empirical Hessian and gradient matrices. We prove that in two canonical classification tasks for mu...
详细信息
An exhaustive and comprehensive investigation was undertaken to address the critical issue of disease detection on apple leaves using cutting-edge deep learning techniques. The research delved into an array of diverse...
详细信息
An exhaustive and comprehensive investigation was undertaken to address the critical issue of disease detection on apple leaves using cutting-edge deep learning techniques. The research delved into an array of diverse approaches, meticulously examining their efficacy and performance in disease detection, ultimately offering valuable insights into this vital domain. The research effort was marked by the exploration and application of a wide spectrum of deep learning models, each chosen for its distinct characteristics and potential advantages. The results of this extensive work were nothing short of remarkable. This study uses state-of-the-art deep learning techniques to present a thorough and rigorous analysis into the important problem of disease detection on apple leaves. Our research covers a wide range of approaches, all of which have been thoroughly assessed for their efficacy in the diagnosis of disease. We used a wide range of deep learning models, selected for their special qualities and possible benefits. The results of this extensive study are impressive and measurable. VGG-INCEP, the top approach, showed exceptional performance with a measured accuracy rate of 97%. The quantification of precision, recall, and F1 scores were 0.94, 0.92, and 0.92, respectively. Similarly, InceptionV3 yielded an F1 score of 0.93, precision of 0.95, and recall of 0.91, in addition to a measured accuracy of 97%. AlexNet consistently demonstrated measurable high precision (0.95) and recall (0.93), resulting in an F1 score of 0.93, despite a somewhat lower accuracy of 87%. The method's balanced performance is highlighted by these metrics. The study also evaluated the effectiveness of SVM, MobileNet, RCNN, and a recommended method. With quantifiable accuracy of 98% and quantifiable precision, recall, and F1 scores of 0.96, the suggested technique stood out. This assessment unequivocally shows that the suggested approach produces the best accuracy and overall performance and is di
暂无评论